{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 国家统计局数据分析\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本作业使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #e5f1fe;\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #effee2;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 50%;\n",
       "    height: auto;\n",
       "    float: center;\n",
       "}\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本作业使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #effee2;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 50%;\n",
    "    height: auto;\n",
    "    float: center;\n",
    "}\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zb</th>\n",
       "      <th>reg</th>\n",
       "      <th>sj</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A010101</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">110000</th>\n",
       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A0S0B05</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">650000</th>\n",
       "      <th>2013</th>\n",
       "      <td>33.856600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>24.914044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>908300 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          data\n",
       "zb      reg    sj             \n",
       "A010101 110000 2018        NaN\n",
       "               2017        NaN\n",
       "               2016        NaN\n",
       "               2015        NaN\n",
       "               2014        NaN\n",
       "...                        ...\n",
       "A0S0B05 650000 2013  33.856600\n",
       "               2012  24.914044\n",
       "               2011        NaN\n",
       "               2010        NaN\n",
       "               2009        NaN\n",
       "\n",
       "[908300 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_raw = pd.read_csv (\"fsnd_zb_data.tsv\", encoding = \"utf8\", sep=\"\\t\",keep_default_na=False,na_values='na_rep',index_col=[0,1,2])\n",
    "df_m = pd.read_csv (\"fsnd_zb_meta.tsv\", encoding = \"utf8\", sep=\"\\t\",keep_default_na=False,na_values='na_rep',index_col=0)\n",
    "df_r = pd.read_csv (\"reg_treeId_level2.tsv\", encoding = \"utf8\", sep=\"\\t\",keep_default_na=False,na_values='na_rep',index_col=3)\n",
    "display(df_raw)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A010101': '地级区划数',\n",
       " 'A010102': '地级市数',\n",
       " 'A010103': '县级区划数',\n",
       " 'A010104': '市辖区数',\n",
       " 'A010105': '县级市数',\n",
       " 'A010106': '县数',\n",
       " 'A010107': '自治县数',\n",
       " 'A010108': '乡镇级区划数',\n",
       " 'A010109': '镇数',\n",
       " 'A01010A': '乡数',\n",
       " 'A01010B': '街道办事处',\n",
       " 'A010201': '三次产业法人单位数',\n",
       " 'A010202': '分机构类型法人单位数',\n",
       " 'A010203': '分行业法人单位数',\n",
       " 'A010301': '按控股情况分企业法人单位数',\n",
       " 'A010302': '按登记注册类型分企业法人单位数',\n",
       " 'A020101': '地区生产总值',\n",
       " 'A020102': '第一产业增加值',\n",
       " 'A020103': '第二产业增加值',\n",
       " 'A020104': '第三产业增加值',\n",
       " 'A020105': '农林牧渔业增加值',\n",
       " 'A020106': '工业增加值',\n",
       " 'A020107': '建筑业增加值',\n",
       " 'A020108': '批发和零售业增加值',\n",
       " 'A020109': '批发和零售贸易餐饮业增加值',\n",
       " 'A02010A': '交通运输、仓储和邮政业增加值',\n",
       " 'A02010B': '交通运输、仓储和邮电通信业增加值',\n",
       " 'A02010C': '住宿和餐饮业增加值',\n",
       " 'A02010D': '金融业增加值',\n",
       " 'A02010E': '房地产业增加值',\n",
       " 'A02010F': '其他行业增加值',\n",
       " 'A02010G': '人均地区生产总值',\n",
       " 'A020201': '地区生产总值指数(上年=100)',\n",
       " 'A020202': '第一产业增加值指数(上年=100)',\n",
       " 'A020203': '第二产业增加值指数(上年=100)',\n",
       " 'A020204': '第三产业增加值指数(上年=100)',\n",
       " 'A020301': '收入法生产总值',\n",
       " 'A020302': '劳动者报酬',\n",
       " 'A020303': '生产税净额',\n",
       " 'A020304': '固定资产折旧',\n",
       " 'A020305': '营业盈余',\n",
       " 'A020401': '支出法生产总值',\n",
       " 'A020402': '最终消费',\n",
       " 'A020403': '居民消费',\n",
       " 'A020404': '农村居民消费',\n",
       " 'A020405': '城镇居民消费',\n",
       " 'A020406': '政府消费',\n",
       " 'A020407': '资本形成总额',\n",
       " 'A020408': '固定资本形成总额',\n",
       " 'A020409': '存货增加',\n",
       " 'A02040A': '货物和服务净流出',\n",
       " 'A02040B': '最终消费率',\n",
       " 'A02040C': '资本形成率',\n",
       " 'A020501': '居民消费水平',\n",
       " 'A020502': '农村居民消费水平',\n",
       " 'A020503': '城镇居民消费水平',\n",
       " 'A020504': '居民消费水平指数(上年=100)',\n",
       " 'A020505': '农村居民消费水平指数(上年=100)',\n",
       " 'A020506': '城镇居民消费水平指数(上年=100)',\n",
       " 'A030101': '年末常住人口',\n",
       " 'A030102': '城镇人口',\n",
       " 'A030103': '乡村人口',\n",
       " 'A030201': '人口出生率',\n",
       " 'A030202': '人口死亡率',\n",
       " 'A030203': '人口自然增长率',\n",
       " 'A030301': '平均预期寿命',\n",
       " 'A030302': '男性平均预期寿命',\n",
       " 'A030303': '女性平均预期寿命',\n",
       " 'A030801': '人口数(人口抽样调查)',\n",
       " 'A030802': '户数、户人口及户规模(人口抽样调查)',\n",
       " 'A030803': '按户口登记状况分人口数(人口抽样调查)',\n",
       " 'A030804': '人口年龄构成和抚养比(人口抽样调查)',\n",
       " 'A030805': '按婚姻状况分人口数(人口抽样调查)',\n",
       " 'A030806': '按受教育程度分人口数(人口抽样调查)',\n",
       " 'A030807': '15岁及以上文盲人口(人口抽样调查)',\n",
       " 'A030808': '按家庭户规模分的户数(人口抽样调查)',\n",
       " 'A040101': '城镇单位就业人员',\n",
       " 'A040102': '农林牧渔业城镇单位就业人员',\n",
       " 'A040103': '采矿业城镇单位就业人员',\n",
       " 'A040104': '制造业城镇单位就业人员',\n",
       " 'A040105': '电力、燃气及水的生产和供应业城镇单位就业人员',\n",
       " 'A040106': '建筑业城镇单位就业人员',\n",
       " 'A040107': '交通运输、仓储及邮电通信业城镇单位就业人员',\n",
       " 'A040108': '信息传输、计算机服务和软件业城镇单位就业人员',\n",
       " 'A040109': '批发和零售业城镇单位就业人员',\n",
       " 'A04010A': '住宿和餐饮业城镇单位就业人员',\n",
       " 'A04010B': '金融业城镇单位就业人员',\n",
       " 'A04010C': '房地产业城镇单位就业人员',\n",
       " 'A04010D': '租赁和商务服务业城镇单位就业人员',\n",
       " 'A04010E': '科学研究、技术服务和地质勘查业城镇单位就业人员',\n",
       " 'A04010F': '水利、环境和公共设施管理业城镇单位就业人员',\n",
       " 'A04010G': '居民服务和其他服务业城镇单位就业人员',\n",
       " 'A04010H': '教育业城镇单位就业人员',\n",
       " 'A04010I': '卫生、社会保障和社会福利业城镇单位就业人员',\n",
       " 'A04010J': '文化、体育和娱乐业城镇单位就业人员',\n",
       " 'A04010K': '公共管理和社会组织城镇单位就业人员',\n",
       " 'A040201': '私营企业和个体就业人员',\n",
       " 'A040202': '制造业私营企业和个体就业人员',\n",
       " 'A040203': '建筑业私营企业和个体就业人员',\n",
       " 'A040204': '交通运输、仓储和邮政业私营企业和个体就业人员',\n",
       " 'A040205': '批发和零售业私营企业和个体就业人员',\n",
       " 'A040206': '住宿和餐饮业私营企业和个体就业人员',\n",
       " 'A040207': '租赁和商务服务业私营企业和个体就业人员',\n",
       " 'A040208': '居民服务和其他服务业私营企业和个体就业人员',\n",
       " 'A040301': '城镇私营企业和个体就业人员',\n",
       " 'A040302': '制造业城镇私营企业和个体就业人员',\n",
       " 'A040303': '建筑业城镇私营企业和个体就业人员',\n",
       " 'A040304': '交通运输、仓储和邮政业城镇私营企业和个体就业人员',\n",
       " 'A040305': '批发和零售业城镇私营企业和个体就业人员',\n",
       " 'A040306': '住宿和餐饮业城镇私营企业和个体就业人员',\n",
       " 'A040307': '租赁和商务服务业城镇私营企业和个体就业人员',\n",
       " 'A040308': '居民服务和其他服务业城镇私营企业和个体就业人员',\n",
       " 'A040401': '私营企业户数',\n",
       " 'A040402': '私营企业就业人数',\n",
       " 'A040403': '私营企业投资者就业人数',\n",
       " 'A040404': '城镇私营企业就业人数',\n",
       " 'A040405': '城镇私营企业投资者就业人数',\n",
       " 'A040406': '乡村私营企业就业人数',\n",
       " 'A040407': '乡村私营企业投资者就业人数',\n",
       " 'A040501': '个体户数',\n",
       " 'A040502': '个体就业人数',\n",
       " 'A040503': '城镇就业人数',\n",
       " 'A040504': '乡村个体就业人数',\n",
       " 'A040601': '城镇单位就业人员工资总额',\n",
       " 'A040602': '国有城镇单位就业人员工资总额',\n",
       " 'A040603': '城镇集体单位就业人员工资总额',\n",
       " 'A040604': '其他城镇单位就业人员工资总额',\n",
       " 'A040605': '城镇单位就业人员工资总额指数(上年=100)',\n",
       " 'A040606': '国有城镇单位就业人员工资总额指数(上年=100)',\n",
       " 'A040607': '城镇集体单位就业人员工资总额指数(上年=100)',\n",
       " 'A040608': '其他城镇单位就业人员工资总额指数(上年=100)',\n",
       " 'A040701': '城镇单位就业人员平均工资',\n",
       " 'A040702': '城镇单位在岗职工平均工资',\n",
       " 'A040703': '城镇国有单位就业人员平均工资',\n",
       " 'A040704': '城镇集体单位就业人员平均工资',\n",
       " 'A040705': '城镇其他单位就业人员平均工资',\n",
       " 'A040706': '城镇单位就业人员平均货币工资指数(上年=100)',\n",
       " 'A040707': '城镇单位在岗职工平均货币工资指数(上年=100)',\n",
       " 'A040708': '国有城镇单位就业人员平均货币工资指数(上年=100)',\n",
       " 'A040709': '城镇集体单位就业人员平均货币工资指数(上年=100)',\n",
       " 'A04070A': '其他城镇单位就业人员平均货币工资指数(上年=100)',\n",
       " 'A04070B': '城镇单位就业人员平均实际工资指数(上年=100)',\n",
       " 'A04070C': '城镇单位在岗职工平均实际工资指数(上年=100)',\n",
       " 'A04070D': '国有城镇单位就业人员平均实际工资指数(上年=100)',\n",
       " 'A04070E': '城镇集体单位就业人员平均实际工资指数(上年=100)',\n",
       " 'A04070F': '其他城镇单位就业人员平均实际工资指数(上年=100)',\n",
       " 'A040801': '城镇单位就业人员平均工资',\n",
       " 'A040802': '国有单位就业人员平均工资',\n",
       " 'A040803': '城镇集体单位就业人员平均工资',\n",
       " 'A040804': '股份合作单位就业人员平均工资',\n",
       " 'A040805': '联营单位就业人员平均工资',\n",
       " 'A040806': '有限责任公司就业人员平均工资',\n",
       " 'A040807': '股份有限公司就业人员平均工资',\n",
       " 'A040808': '其他单位就业人员平均工资',\n",
       " 'A040809': '港、澳、台商投资单位就业人员平均工资',\n",
       " 'A04080A': '外商投资单位就业人员平均工资',\n",
       " 'A040901': '城镇单位就业人员工资总额',\n",
       " 'A040902': '农、林、牧、渔业城镇单位就业人员工资总额',\n",
       " 'A040903': '采矿业城镇单位就业人员工资总额',\n",
       " 'A040904': '制造业城镇单位就业人员工资总额',\n",
       " 'A040905': '电力、燃气及水的生产和供应业城镇单位就业人员工资总额',\n",
       " 'A040906': '建筑业城镇单位就业人员工资总额',\n",
       " 'A040907': '交通运输、仓储和邮政业城镇单位就业人员工资总额',\n",
       " 'A040908': '信息传输、计算机服务和软件业城镇单位就业人员工资总额',\n",
       " 'A040909': '批发和零售业城镇单位就业人员工资总额',\n",
       " 'A04090A': '住宿和餐饮业城镇单位就业人员工资总额',\n",
       " 'A04090B': '金融业城镇单位就业人员工资总额',\n",
       " 'A04090C': '房地产业城镇单位就业人员工资总额',\n",
       " 'A04090D': '租赁和商务服务业城镇单位就业人员工资总额',\n",
       " 'A04090E': '科学研究、技术服务和地质勘查业城镇单位就业人员工资总额',\n",
       " 'A04090F': '水利、环境和公共设施管理业城镇单位就业人员工资总额',\n",
       " 'A04090G': '居民服务和其他服务业城镇单位就业人员工资总额',\n",
       " 'A04090H': '教育城镇单位就业人员工资总额',\n",
       " 'A04090I': '卫生、社会保障和社会福利业城镇单位就业人员工资总额',\n",
       " 'A04090J': '文化、体育和娱乐业城镇单位就业人员工资总额',\n",
       " 'A04090K': '公共管理和社会组织城镇单位就业人员工资总额',\n",
       " 'A040A01': '城镇单位就业人员平均工资',\n",
       " 'A040A02': '农、林、牧、渔业城镇单位就业人员平均工资',\n",
       " 'A040A03': '采矿业城镇单位就业人员平均工资',\n",
       " 'A040A04': '制造业城镇单位就业人员平均工资',\n",
       " 'A040A05': '电力、燃气及水的生产和供应业城镇单位就业人员平均工资',\n",
       " 'A040A06': '建筑业城镇单位就业人员平均工资',\n",
       " 'A040A07': '交通运输、仓储和邮政业城镇单位就业人员平均工资',\n",
       " 'A040A08': '信息传输、计算机服务和软件业城镇单位就业人员平均工资',\n",
       " 'A040A09': '批发和零售业城镇单位就业人员平均工资',\n",
       " 'A040A0A': '住宿和餐饮业城镇单位就业人员平均工资',\n",
       " 'A040A0B': '金融业城镇单位就业人员平均工资',\n",
       " 'A040A0C': '房地产业城镇单位就业人员平均工资',\n",
       " 'A040A0D': '租赁和商务服务业城镇单位就业人员平均工资',\n",
       " 'A040A0E': '科学研究、技术服务和地质勘查业城镇单位就业人员平均工资',\n",
       " 'A040A0F': '水利、环境和公共设施管理业城镇单位就业人员平均工资',\n",
       " 'A040A0G': '居民服务和其他服务业城镇单位就业人员平均工资',\n",
       " 'A040A0H': '教育城镇单位就业人员平均工资',\n",
       " 'A040A0I': '卫生、社会保障和社会福利业城镇单位就业人员平均工资',\n",
       " 'A040A0J': '文化、体育和娱乐业城镇单位就业人员平均工资',\n",
       " 'A040A0K': '公共管理和社会组织城镇单位就业人员平均工资',\n",
       " 'A040B01': '城镇私营单位就业人员平均工资',\n",
       " 'A040B02': '农、林、牧、渔业城镇私营单位就业人员平均工资',\n",
       " 'A040B03': '采矿业城镇私营单位就业人员平均工资',\n",
       " 'A040B04': '制造业城镇私营单位就业人员平均工资',\n",
       " 'A040B05': '电力、燃气及水的生产和供应业城镇私营单位就业人员平均工资',\n",
       " 'A040B06': '建筑业城镇私营单位就业人员平均工资',\n",
       " 'A040B07': '交通运输、仓储和邮政业城镇私营单位就业人员平均工资',\n",
       " 'A040B08': '信息传输、计算机服务和软件业城镇私营单位就业人员平均工资',\n",
       " 'A040B09': '批发和零售业城镇私营单位就业人员平均工资',\n",
       " 'A040B0A': '住宿和餐饮业城镇私营单位就业人员平均工资',\n",
       " 'A040B0B': '金融业城镇私营单位就业人员平均工资',\n",
       " 'A040B0C': '房地产业城镇私营单位就业人员平均工资',\n",
       " 'A040B0D': '租赁和商务服务业城镇私营单位就业人员平均工资',\n",
       " 'A040B0E': '科学研究、技术服务和地质勘查业城镇私营单位就业人员平均工资',\n",
       " 'A040B0F': '水利、环境和公共设施管理业城镇私营单位就业人员平均工资',\n",
       " 'A040B0G': '居民服务和其他服务业城镇私营单位就业人员平均工资',\n",
       " 'A040B0H': '教育城镇私营单位就业人员平均工资',\n",
       " 'A040B0I': '卫生、社会保障和社会福利业城镇私营单位就业人员平均工资',\n",
       " 'A040B0J': '文化、体育和娱乐业城镇私营单位就业人员平均工资',\n",
       " 'A040B0K': '公共管理和社会组织城镇私营单位就业人员平均工资',\n",
       " 'A040C01': '城镇登记失业人数',\n",
       " 'A040C02': '城镇登记失业率',\n",
       " 'A050101': '全社会固定资产投资',\n",
       " 'A050102': '城镇固定资产投资',\n",
       " 'A050103': '房地产开发投资',\n",
       " 'A050201': '全社会固定资产投资',\n",
       " 'A050202': '内资企业全社会固定资产投资',\n",
       " 'A050203': '国有全社会固定资产投资',\n",
       " 'A050204': '集体全社会固定资产投资',\n",
       " 'A050205': '股份合作全社会固定资产投资',\n",
       " 'A050206': '联营全社会固定资产投资',\n",
       " 'A050207': '有限责任公司全社会固定资产投资',\n",
       " 'A050208': '股份有限公司全社会固定资产投资',\n",
       " 'A050209': '私营全社会固定资产投资',\n",
       " 'A05020A': '个体全社会固定资产投资',\n",
       " 'A05020B': '其他全社会固定资产投资',\n",
       " 'A05020C': '港、澳、台商投资全社会固定资产投资',\n",
       " 'A05020D': '外商投资全社会固定资产投资',\n",
       " 'A050301': '全社会固定资产本年资金来源小计',\n",
       " 'A050302': '全社会固定资产投资中国家预算内资金',\n",
       " 'A050303': '全社会固定资产投资中国内贷款',\n",
       " 'A050304': '全社会固定资产投资中利用外资',\n",
       " 'A050305': '全社会固定资产投资中自筹资金',\n",
       " 'A050306': '全社会固定资产投资中其他资金',\n",
       " 'A050401': '全社会住宅投资',\n",
       " 'A050402': '城镇住宅投资',\n",
       " 'A050403': '房地产住宅投资',\n",
       " 'A050501': '全社会固定资产投资',\n",
       " 'A050502': '农、林、牧、渔业全社会固定资产投资',\n",
       " 'A050503': '采矿业全社会固定资产投资',\n",
       " 'A050504': '制造业全社会固定资产投资',\n",
       " 'A050505': '电力、燃气及水的生产和供应业全社会固定资产投资',\n",
       " 'A050506': '建筑业全社会固定资产投资',\n",
       " 'A050507': '交通运输、仓储和邮政业全社会固定资产投资',\n",
       " 'A050508': '信息传输计算机服务和软件业全社会固定资产投资',\n",
       " 'A050509': '批发和零售业全社会固定资产投资',\n",
       " 'A05050A': '住宿和餐饮业全社会固定资产投资',\n",
       " 'A05050B': '金融业全社会固定资产投资',\n",
       " 'A05050C': '房地产业全社会固定资产投资',\n",
       " 'A05050D': '租赁和商务服务业全社会固定资产投资',\n",
       " 'A05050E': '科学研究、技术服务和地质勘查业全社会固定资产投资',\n",
       " 'A05050F': '水利、环境和公共设施管理业全社会固定资产投资',\n",
       " 'A05050G': '居民服务和其他服务业全社会固定资产投资',\n",
       " 'A05050H': '教育全社会固定资产投资',\n",
       " 'A05050I': '卫生、社会保障和社会福利业全社会固定资产投资',\n",
       " 'A05050J': '文化、体育和娱乐业全社会固定资产投资',\n",
       " 'A05050K': '公共管理和社会组织全社会固定资产投资',\n",
       " 'A05050L': '国际组织全社会固定资产投资',\n",
       " 'A050601': '全社会建设总规模',\n",
       " 'A050602': '全社会在建总规模',\n",
       " 'A050603': '全社会在建净规模',\n",
       " 'A050701': '房屋施工面积',\n",
       " 'A050702': '住宅房屋施工面积',\n",
       " 'A050703': '商品住宅房屋施工面积',\n",
       " 'A050704': '房屋竣工面积',\n",
       " 'A050705': '住宅房屋竣工面积',\n",
       " 'A050706': '商品住宅房屋竣工面积',\n",
       " 'A050707': '房屋竣工价值',\n",
       " 'A050708': '住宅房屋竣工价值',\n",
       " 'A050709': '商品住宅房屋竣工价值',\n",
       " 'A050801': '固定资产投资(不含农户)国家预算内资金',\n",
       " 'A050802': '固定资产投资(不含农户)国内贷款',\n",
       " 'A050803': '固定资产投资(不含农户)利用外资',\n",
       " 'A050804': '固定资产投资(不含农户)自筹资金',\n",
       " 'A050805': '固定资产投资(不含农户)其他资金',\n",
       " 'A050901': '固定资产投资(不含农户)中央项目',\n",
       " 'A050902': '固定资产投资(不含农户)地方项目',\n",
       " 'A050A01': '固定资产投资(不含农户)',\n",
       " 'A050A02': '固定资产投资(不含农户)建筑安装工程',\n",
       " 'A050A03': '固定资产投资(不含农户)设备、工器具购置',\n",
       " 'A050A04': '固定资产投资(不含农户)其他费用',\n",
       " 'A050A05': '新建固定资产投资(不含农户)',\n",
       " 'A050A06': '扩建固定资产投资(不含农户)',\n",
       " 'A050A07': '改建固定资产投资(不含农户)',\n",
       " 'A050B01': '固定资产投资(不含农户)建设总规模',\n",
       " 'A050B02': '固定资产投资(不含农户)在建总规模',\n",
       " 'A050B03': '固定资产投资(不含农户)在建净规模',\n",
       " 'A050C01': '固定资产投资(不含农户)',\n",
       " 'A050C02': '农、林、牧、渔业固定资产投资(不含农户)',\n",
       " 'A050C03': '采矿业固定资产投资(不含农户)',\n",
       " 'A050C04': '制造业固定资产投资(不含农户)',\n",
       " 'A050C05': '电力、燃气及水的生产和供应业固定资产投资(不含农户)',\n",
       " 'A050C06': '建筑业固定资产投资(不含农户)',\n",
       " 'A050C07': '交通运输仓储和邮政业固定资产投资(不含农户)',\n",
       " 'A050C08': '信息传输、计算机服务和软件业固定资产投资(不含农户)',\n",
       " 'A050C09': '批发和零售业固定资产投资(不含农户)',\n",
       " 'A050C0A': '住宿和餐饮业固定资产投资(不含农户)',\n",
       " 'A050C0B': '金融业固定资产投资(不含农户)',\n",
       " 'A050C0C': '房地产业固定资产投资(不含农户)',\n",
       " 'A050C0D': '租赁和商务服务业固定资产投资(不含农户)',\n",
       " 'A050C0E': '科学研究、技术服务和地质勘查业固定资产投资(不含农户)',\n",
       " 'A050C0F': '水利、环境和公共设施管理业固定资产投资(不含农户)',\n",
       " 'A050C0G': '居民服务和其他服务业固定资产投资(不含农户)',\n",
       " 'A050C0H': '教育固定资产投资(不含农户)',\n",
       " 'A050C0I': '卫生、社会保障和社会福利业固定资产投资(不含农户)',\n",
       " 'A050C0J': '文化、体育和娱乐业固定资产投资(不含农户)',\n",
       " 'A050C0K': '公共管理和社会组织固定资产投资(不含农户)',\n",
       " 'A050C0L': '国际组织固定资产投资(不含农户)',\n",
       " 'A050D01': '500万元以下固定资产投资(不含农户)',\n",
       " 'A050D02': '500万元-1亿元固定资产投资(不含农户)',\n",
       " 'A050D03': '1-5亿元固定资产投资(不含农户)',\n",
       " 'A050D04': '5-10亿元固定资产投资(不含农户)',\n",
       " 'A050D05': '10亿元以上固定资产投资(不含农户)',\n",
       " 'A050E01': '能源工业固定资产投资(不含农户)',\n",
       " 'A050E02': '煤炭开采及洗选业固定资产投资(不含农户)',\n",
       " 'A050E03': '石油及天然气开采业固定资产投资(不含农户)',\n",
       " 'A050E04': '石油及炼焦加工业固定资产投资(不含农户)',\n",
       " 'A050E05': '电力、热力及燃气的生产和供应业固定资产投资(不含农户)',\n",
       " 'A050F01': '固定资产投资(不含农户)房屋施工面积',\n",
       " 'A050F02': '固定资产投资(不含农户)住宅施工面积',\n",
       " 'A050F03': '固定资产投资(不含农户)房屋竣工面积',\n",
       " 'A050F04': '固定资产投资(不含农户)住宅竣工面积',\n",
       " 'A050F05': '固定资产投资(不含农户)竣工房屋价值',\n",
       " 'A050F06': '固定资产投资(不含农户)竣工住宅价值',\n",
       " 'A050G01': '新增固定资产投资(不含农户)',\n",
       " 'A050G02': '农、林、牧、渔业新增固定资产投资(不含农户)',\n",
       " 'A050G03': '采掘业新增固定资产投资(不含农户)',\n",
       " 'A050G04': '制造业新增固定资产投资(不含农户)',\n",
       " 'A050G05': '电力、煤气及水的生产和供应业新增固定资产投资(不含农户)',\n",
       " 'A050G06': '建筑业新增固定资产投资(不含农户)',\n",
       " 'A050G07': '交通运输仓储和邮政业新增固定资产投资(不含农户)',\n",
       " 'A050G08': '信息传输、计算机服务和软件业新增固定资产投资(不含农户)',\n",
       " 'A050G09': '批发和零售业新增固定资产投资(不含农户)',\n",
       " 'A050G0A': '住宿和餐饮业新增固定资产投资(不含农户)',\n",
       " 'A050G0B': '金融业新增固定资产投资(不含农户)',\n",
       " 'A050G0C': '房地产业新增固定资产投资(不含农户)',\n",
       " 'A050G0D': '租赁和商务服务业新增固定资产投资(不含农户)',\n",
       " 'A050G0E': '科学研究、技术服务和地质勘查业新增固定资产投资(不含农户)',\n",
       " 'A050G0F': '水利、环境和公共设施管理业新增固定资产投资(不含农户)',\n",
       " 'A050G0G': '居民服务和其他服务业新增固定资产投资(不含农户)',\n",
       " 'A050G0H': '教育新增固定资产投资(不含农户)',\n",
       " 'A050G0I': '卫生、社会保障和社会福利业新增固定资产投资(不含农户)',\n",
       " 'A050G0J': '文化、体育和娱乐业新增固定资产投资(不含农户)',\n",
       " 'A050G0K': '公共管理和社会组织新增固定资产投资(不含农户)',\n",
       " 'A050G0L': '国际组织新增固定资产投资(不含农户)',\n",
       " 'A050H01': '固定资产投资(不含农户)施工项目个数',\n",
       " 'A050H02': '固定资产投资(不含农户)新开工项目个数',\n",
       " 'A050H03': '固定资产投资(不含农户)全部建成投产项目个数',\n",
       " 'A050H04': '固定资产投资(不含农户)项目建成投产率',\n",
       " 'A050I01': '固定资产投资(不含农户)',\n",
       " 'A050I02': '固定资产投资(不含农户)新增固定资产',\n",
       " 'A050I03': '固定资产投资(不含农户)交付使用率',\n",
       " 'A050J01': '农村农户固定资产投资额',\n",
       " 'A050J02': '农村农户竣工房屋投资额',\n",
       " 'A050J03': '农村农户竣工住宅投资额',\n",
       " 'A050J04': '农村农户施工房屋建筑面积',\n",
       " 'A050J05': '农村农户竣工房屋建筑面积',\n",
       " 'A050J06': '农村农户竣工住宅建筑面积',\n",
       " 'A050J07': '农村农户竣工房屋造价',\n",
       " 'A050J08': '农村农户竣工住宅造价',\n",
       " 'A051C01': '房地产开发企业个数',\n",
       " 'A051C02': '内资房地产开发企业个数',\n",
       " 'A051C03': '国有房地产开发企业个数',\n",
       " 'A051C04': '集体房地产开发企业个数',\n",
       " 'A051C05': '港、澳、台投资房地产开发企业个数',\n",
       " 'A051C06': '外商投资房地产开发企业个数',\n",
       " 'A051D01': '房地产开发企业平均从业人数',\n",
       " 'A051D02': '内资房地产开发企业平均从业人数',\n",
       " 'A051D03': '国有房地产开发企业平均从业人数',\n",
       " 'A051D04': '集体房地产开发企业平均从业人数',\n",
       " 'A051D05': '港、澳、台投资房地产开发企业平均从业人数',\n",
       " 'A051D06': '外商投资房地产开发企业平均从业人数',\n",
       " 'A051E01': '房地产开发企业待开发土地面积',\n",
       " 'A051E02': '房地产开发企业购置土地面积',\n",
       " 'A051E03': '房地产开发企业土地成交价款',\n",
       " 'A051E04': '房地产开发企业土地购置费用',\n",
       " 'A051F01': '房地产开发企业计划总投资',\n",
       " 'A051F02': '房地产开发企业自开始建设至本年底累计完成投资',\n",
       " 'A051F03': '房地产开发企业本年完成投资额',\n",
       " 'A051F04': '房地产开发企业建筑安装工程本年完成投资额',\n",
       " 'A051F05': '房地产开发企业设备工器具购置本年完成投资额',\n",
       " 'A051F06': '房地产开发企业其他费用本年完成投资额',\n",
       " 'A051G01': '房地产开发投资额',\n",
       " 'A051G02': '房地产开发住宅投资额',\n",
       " 'A051G03': '房地产开发别墅、高档公寓投资额',\n",
       " 'A051G04': '房地产开发办公楼投资额',\n",
       " 'A051G05': '房地产开发商业营业用房投资额',\n",
       " 'A051G06': '房地产开发其他投资额',\n",
       " 'A051H01': '房地产开发企业本年实际到位资金',\n",
       " 'A051H02': '房地产开发企业国内贷款',\n",
       " 'A051H03': '房地产开发企业利用外资',\n",
       " 'A051H04': '房地产开发企业外商直接投资',\n",
       " 'A051H05': '房地产开发企业自筹资金',\n",
       " 'A051H06': '房地产开发企业其他资金来源',\n",
       " 'A051H07': '房地产开发企业定金及预收款',\n",
       " 'A051H08': '房地产开发企业个人按揭贷款',\n",
       " 'A051H09': '房地产开发企业其他到位资金',\n",
       " 'A051I01': '房地产开发企业施工房屋面积',\n",
       " 'A051I02': '房地产开发企业竣工房屋面积',\n",
       " 'A051I03': '房地产开发企业房屋建筑面积竣工率',\n",
       " 'A051I04': '房地产开发企业竣工房屋价值',\n",
       " 'A051I05': '房地产开发企业竣工房屋造价',\n",
       " 'A051J01': '房地产开发企业新开工房屋面积',\n",
       " 'A051J02': '房地产开发企业住宅新开工房屋面积',\n",
       " 'A051J03': '房地产开发企业别墅、高档公寓新开工房屋面积',\n",
       " 'A051J04': '房地产开发企业办公楼新开工房屋面积',\n",
       " 'A051J05': '房地产开发企业商业营业用房新开工房屋面积',\n",
       " 'A051J06': '房地产开发企业其他用途新开工房屋面积',\n",
       " 'A051K01': '商品房销售面积',\n",
       " 'A051K02': '住宅商品房销售面积',\n",
       " 'A051K03': '别墅、高档公寓销售面积',\n",
       " 'A051K04': '办公楼商品房销售面积',\n",
       " 'A051K05': '商业营业用房销售面积',\n",
       " 'A051K06': '其他商品房销售面积',\n",
       " 'A051L01': '商品房销售额',\n",
       " 'A051L02': '住宅商品房销售额',\n",
       " 'A051L03': '别墅、高档公寓销售额',\n",
       " 'A051L04': '办公楼销售额',\n",
       " 'A051L05': '商业营业用房销售额',\n",
       " 'A051L06': '其他商品房销售额',\n",
       " 'A051M01': '商品房平均销售价格',\n",
       " 'A051M02': '住宅商品房平均销售价格',\n",
       " 'A051M03': '别墅、高档公寓平均销售价格',\n",
       " 'A051M04': '办公楼商品房平均销售价格',\n",
       " 'A051M05': '商业营业用房平均销售价格',\n",
       " 'A051M06': '其他商品房平均销售价格',\n",
       " 'A051N01': '房地产开发企业实收资本',\n",
       " 'A051N02': '房地产开发企业资产总计',\n",
       " 'A051N03': '房地产开发企业累计折旧',\n",
       " 'A051N04': '房地产开发企业本年折旧',\n",
       " 'A051N05': '房地产开发企业负债合计',\n",
       " 'A051N06': '房地产开发企业所有者权益',\n",
       " 'A051N07': '房地产开发企业资产负债率',\n",
       " 'A051O01': '房地产开发企业主营业务收入',\n",
       " 'A051O02': '房地产开发企业土地转让收入',\n",
       " 'A051O03': '房地产开发企业商品房销售收入',\n",
       " 'A051O04': '房地产开发企业房屋出租收入',\n",
       " 'A051O05': '房地产开发企业其他收入',\n",
       " 'A051O06': '房地产开发企业主营业务税金及附加',\n",
       " 'A051O07': '房地产开发企业营业利润',\n",
       " 'A051P01': '500万元以下房地产开发投资额',\n",
       " 'A051P02': '500-1000万元房地产开发投资额',\n",
       " 'A051P03': '1000-3000万元房地产开发投资额',\n",
       " 'A051P04': '3000-5000万元房地产开发投资额',\n",
       " 'A051P05': '5000万-1亿元房地产开发投资额',\n",
       " 'A051P06': '1-5亿元房地产开发投资额',\n",
       " 'A051P07': '5-10亿元房地产开发投资额',\n",
       " 'A051P08': '10亿元以上房地产开发投资额',\n",
       " 'A051Q01': '房地产开发企业住宅竣工套数',\n",
       " 'A051Q02': '房地产开发企业别墅、高档公寓竣工套数',\n",
       " 'A051Q03': '房地产开发企业住宅销售套数',\n",
       " 'A051Q04': '房地产开发企业别墅、高档公寓销售套数',\n",
       " 'A060101': '经营单位所在地进出口总额',\n",
       " 'A060102': '经营单位所在地出口总额',\n",
       " 'A060103': '经营单位所在地进口总额',\n",
       " 'A060201': '境内目的地和货源地进出口总额',\n",
       " 'A060202': '境内目的地和货源地出口总额',\n",
       " 'A060203': '境内目的地和货源地进口总额',\n",
       " 'A060301': '外商投资企业进出口总额',\n",
       " 'A060302': '外商投资企业出口总额',\n",
       " 'A060303': '外商投资企业进口总额',\n",
       " 'A060401': '外商投资企业数',\n",
       " 'A060402': '外商投资企业投资总额',\n",
       " 'A060403': '外商投资企业注册资本',\n",
       " 'A060404': '外方外商投资企业注册资本',\n",
       " 'A070101': '国有经济能源工业固定资产投资',\n",
       " 'A070102': '国有经济煤炭采选业固定资产投资',\n",
       " 'A070103': '国有经济石油和天然气开采业固定资产投资',\n",
       " 'A070104': '国有经济电力、蒸汽、热水生产和供应业固定资产投资',\n",
       " 'A070105': '国有经济石油加工及炼焦业固定资产投资',\n",
       " 'A070106': '国有经济煤气生产和供应业固定资产投资',\n",
       " 'A070201': '能源工业投资',\n",
       " 'A070202': '煤炭采选业投资',\n",
       " 'A070203': '石油和天然气开采业投资',\n",
       " 'A070204': '电力、蒸汽、热水生产和供应业投资',\n",
       " 'A070205': '石油加工及炼焦业投资',\n",
       " 'A070206': '煤气生产和供应业投资',\n",
       " 'A070301': '焦炭生产量',\n",
       " 'A070302': '原油生产量',\n",
       " 'A070303': '汽油生产量',\n",
       " 'A070304': '煤油生产量',\n",
       " 'A070305': '柴油生产量',\n",
       " 'A070306': '燃料油生产量',\n",
       " 'A070307': '天然气生产量',\n",
       " 'A070308': '发电量',\n",
       " 'A070309': '水力发电量',\n",
       " 'A07030A': '火力发电量',\n",
       " 'A070401': '城市天然气供气总量',\n",
       " 'A070402': '城市天然气用气人口',\n",
       " 'A070403': '城市人工煤气供气总量',\n",
       " 'A070404': '城市人工煤气用气人口',\n",
       " 'A070405': '城市液化石油气供气总量',\n",
       " 'A070406': '城市液化石油气用气人口',\n",
       " 'A070501': '蒸汽供热能力',\n",
       " 'A070502': '热水供热能力',\n",
       " 'A070601': '煤炭消费量',\n",
       " 'A070602': '焦炭消费量',\n",
       " 'A070603': '原油消费量',\n",
       " 'A070604': '汽油消费量',\n",
       " 'A070605': '煤油消费量',\n",
       " 'A070606': '柴油消费量',\n",
       " 'A070607': '燃料油消费量',\n",
       " 'A070608': '天然气消费量',\n",
       " 'A070609': '电力消费量',\n",
       " 'A070701': '单位地区生产总值能耗(等价值)',\n",
       " 'A070702': '单位地区生产总值能耗(等价值)_同比增长',\n",
       " 'A070703': '单位工业增加值能耗(规模以上当量值)',\n",
       " 'A070704': '单位工业增加值能耗(规模以上当量值)_同比增长',\n",
       " 'A070705': '单位地区生产总值电耗(等价值)',\n",
       " 'A070706': '单位地区生产总值电耗(等价值)_同比增长',\n",
       " 'A080101': '地方财政一般预算收入',\n",
       " 'A080102': '地方财政税收收入',\n",
       " 'A080103': '地方财政国内增值税',\n",
       " 'A080104': '地方财政营业税',\n",
       " 'A080105': '地方财政企业所得税',\n",
       " 'A080106': '地方财政个人所得税',\n",
       " 'A080107': '地方财政资源税',\n",
       " 'A080108': '地方财政固定资产投资方向调节税',\n",
       " 'A080109': '地方财政城市维护建设税',\n",
       " 'A08010A': '地方财政房产税',\n",
       " 'A08010B': '地方财政印花税',\n",
       " 'A08010C': '地方财政城镇土地使用税',\n",
       " 'A08010D': '地方财政土地增值税',\n",
       " 'A08010E': '地方财政车船税',\n",
       " 'A08010F': '地方财政耕地占用税',\n",
       " 'A08010G': '地方财政契税',\n",
       " 'A08010H': '地方财政烟叶税',\n",
       " 'A08010I': '地方财政其他税收收入',\n",
       " 'A08010J': '地方财政非税收入',\n",
       " 'A08010K': '地方财政专项收入',\n",
       " 'A08010L': '地方财政行政事业性收费收入',\n",
       " 'A08010M': '地方财政罚没收入',\n",
       " 'A08010N': '地方财政国有资本经营收入',\n",
       " 'A08010O': '地方财政国有资源(资产)有偿使用收入',\n",
       " 'A08010P': '地方财政其他非税收入',\n",
       " 'A080201': '地方财政一般预算支出',\n",
       " 'A080202': '地方财政一般公共服务支出',\n",
       " 'A080203': '地方财政外交支出',\n",
       " 'A080204': '地方财政国防支出',\n",
       " 'A080205': '地方财政公共安全支出',\n",
       " 'A080206': '地方财政教育支出',\n",
       " 'A080207': '地方财政科学技术支出',\n",
       " 'A080208': '地方财政文化体育与传媒支出',\n",
       " 'A080209': '地方财政社会保障和就业支出',\n",
       " 'A08020A': '地方财政医疗卫生支出',\n",
       " 'A08020B': '地方财政环境保护支出',\n",
       " 'A08020C': '地方财政城乡社区事务支出',\n",
       " 'A08020D': '地方财政农林水事务支出',\n",
       " 'A08020E': '地方财政交通运输支出',\n",
       " 'A08020F': '地方财政资源勘探电力信息等事务支出',\n",
       " 'A08020G': '地方财政商业服务业等事务支出',\n",
       " 'A08020H': '地方财政金融监管支出',\n",
       " 'A08020I': '地方财政地震灾后重建支出',\n",
       " 'A08020J': '地方财政国土资源气象等事务支出决策数',\n",
       " 'A08020K': '地方财政住房保障支出支出',\n",
       " 'A08020L': '地方财政粮油物资储备管理等事务',\n",
       " 'A08020M': '地方财政国债还本付息支出',\n",
       " 'A08020N': '地方财政其他支出',\n",
       " 'A090101': '居民消费价格指数(上年=100)',\n",
       " 'A090102': '城市居民消费价格指数(上年=100)',\n",
       " 'A090103': '农村居民消费价格指数(上年=100)',\n",
       " 'A090104': '商品零售价格指数(上年=100)',\n",
       " 'A090105': '城市商品零售价格指数(上年=100)',\n",
       " 'A090106': '农村商品零售价格指数(上年=100)',\n",
       " 'A090201': '居民消费价格指数(上年=100)',\n",
       " 'A090202': '食品类居民消费价格指数(上年=100)',\n",
       " 'A090203': '粮食类居民消费价格指数(上年=100)',\n",
       " 'A090204': '油脂类居民消费价格指数(上年=100)',\n",
       " 'A090205': '肉禽及其制品类居民消费价格指数(上年=100)',\n",
       " 'A090206': '蛋类居民消费价格指数(上年=100)',\n",
       " 'A090207': '水产品类居民消费价格指数(上年=100)',\n",
       " 'A090208': '菜类居民消费价格指数(上年=100)',\n",
       " 'A090209': '鲜菜类居民消费价格指数(上年=100)',\n",
       " 'A09020A': '干鲜瓜果类居民消费价格指数(上年=100)',\n",
       " 'A09020B': '鲜果类居民消费价格指数(上年=100)',\n",
       " 'A09020C': '在外用膳食品类居民消费价格指数(上年=100)',\n",
       " 'A09020D': '烟酒及用品类居民消费价格指数(上年=100)',\n",
       " 'A09020E': '烟草类居民消费价格指数(上年=100)',\n",
       " 'A09020F': '酒类居民消费价格指数(上年=100)',\n",
       " 'A09020G': '衣着类居民消费价格指数(上年=100)',\n",
       " 'A09020H': '服装类居民消费价格指数(上年=100)',\n",
       " 'A09020I': '衣着材料类居民消费价格指数(上年=100)',\n",
       " 'A09020J': '鞋袜帽类居民消费价格指数(上年=100)',\n",
       " 'A09020K': '衣着加工服务费类居民消费价格指数(上年=100)',\n",
       " 'A09020L': '家庭设备用品及服务类居民消费价格指数(上年=100)',\n",
       " 'A09020M': '耐用消费品类居民消费价格指数(上年=100)',\n",
       " 'A09020N': '室内装饰品类居民消费价格指数(上年=100)',\n",
       " 'A09020O': '床上用品类居民消费价格指数(上年=100)',\n",
       " 'A09020P': '家庭日用杂品类居民消费价格指数(上年=100)',\n",
       " 'A09020Q': '家庭服务及加工维修服务费类居民消费价格指数(上年=100)',\n",
       " 'A09020R': '医疗保健和个人用品类居民消费价格指数(上年=100)',\n",
       " 'A09020S': '医疗保健类居民消费价格指数(上年=100)',\n",
       " 'A09020T': '医疗器具及用品类居民消费价格指数(上年=100)',\n",
       " 'A09020U': '中药材及中成药类居民消费价格指数(上年=100)',\n",
       " 'A09020V': '西药类居民消费价格指数(上年=100)',\n",
       " 'A09020W': '保健器具及用品类居民消费价格指数(上年=100)',\n",
       " 'A09020X': '医疗保健服务类居民消费价格指数(上年=100)',\n",
       " 'A09020Y': '个人用品及服务类居民消费价格指数(上年=100)',\n",
       " 'A09020Z': '化妆美容用品类居民消费价格指数(上年=100)',\n",
       " 'A090210': '卫生用品类居民消费价格指数(上年=100)',\n",
       " 'A090211': '个人饰品类居民消费价格指数(上年=100)',\n",
       " 'A090212': '个人服务类居民消费价格指数(上年=100)',\n",
       " 'A090213': '交通和通信类居民消费价格指数(上年=100)',\n",
       " 'A090214': '交通类居民消费价格指数(上年=100)',\n",
       " 'A090215': '交通工具类居民消费价格指数(上年=100)',\n",
       " 'A090216': '车用燃料及零配件类居民消费价格指数(上年=100)',\n",
       " 'A090217': '车辆使用及维修费类居民消费价格指数(上年=100)',\n",
       " 'A090218': '市内公共交通费类居民消费价格指数(上年=100)',\n",
       " 'A090219': '城市间交通费类居民消费价格指数(上年=100)',\n",
       " 'A09021A': '通信类居民消费价格指数(上年=100)',\n",
       " 'A09021B': '通信工具类居民消费价格指数(上年=100)',\n",
       " 'A09021C': '通信服务类居民消费价格指数(上年=100)',\n",
       " 'A09021D': '娱乐教育文化类居民消费价格指数(上年=100)',\n",
       " 'A09021E': '文娱用耐用消费品及服务类居民消费价格指数(上年=100)',\n",
       " 'A09021F': '教育类居民消费价格指数(上年=100)',\n",
       " 'A09021G': '教材及参考书类居民消费价格指数(上年=100)',\n",
       " 'A09021H': '学杂托幼费类居民消费价格指数(上年=100)',\n",
       " 'A09021I': '文化娱乐类居民消费价格指数(上年=100)',\n",
       " 'A09021J': '文化娱乐用品类居民消费价格指数(上年=100)',\n",
       " 'A09021K': '书报杂志类居民消费价格指数(上年=100)',\n",
       " 'A09021L': '文娱费类居民消费价格指数(上年=100)',\n",
       " 'A09021M': '旅游类居民消费价格指数(上年=100)',\n",
       " 'A09021N': '居住类居民消费价格指数(上年=100)',\n",
       " 'A09021O': '建房及装修材料类居民消费价格指数(上年=100)',\n",
       " 'A09021P': '租房类居民消费价格指数(上年=100)',\n",
       " 'A09021Q': '自有住房类居民消费价格指数(上年=100)',\n",
       " 'A09021R': '水电燃料类居民消费价格指数(上年=100)',\n",
       " 'A090301': '农村居民消费价格指数(上年=100)',\n",
       " 'A090302': '食品类农村居民消费价格指数(上年=100)',\n",
       " 'A090303': '粮食类农村居民消费价格指数(上年=100)',\n",
       " 'A090304': '淀粉及制品类农村居民消费价格指数(上年=100)',\n",
       " 'A090305': '干豆类及豆制品类农村居民消费价格指数(上年=100)',\n",
       " 'A090306': '油脂类农村居民消费价格指数(上年=100)',\n",
       " 'A090307': '肉禽及其制品类农村居民消费价格指数(上年=100)',\n",
       " 'A090308': '食用畜肉及副产品类农村居民消费价格指数(上年=100)',\n",
       " 'A090309': '禽类农村居民消费价格指数(上年=100)',\n",
       " 'A09030A': '加工肉禽类农村居民消费价格指数(上年=100)',\n",
       " 'A09030B': '蛋类农村居民消费价格指数(上年=100)',\n",
       " 'A09030C': '水产品类农村居民消费价格指数(上年=100)',\n",
       " 'A09030D': '鱼类农村居民消费价格指数(上年=100)',\n",
       " 'A09030E': '其他水产品类农村居民消费价格指数(上年=100)',\n",
       " 'A09030F': '菜类农村居民消费价格指数(上年=100)',\n",
       " 'A09030G': '调味品类农村居民消费价格指数(上年=100)',\n",
       " 'A09030H': '糖类农村居民消费价格指数(上年=100)',\n",
       " 'A09030I': '茶及饮料类农村居民消费价格指数(上年=100)',\n",
       " 'A09030J': '茶叶类农村居民消费价格指数(上年=100)',\n",
       " 'A09030K': '饮料类农村居民消费价格指数(上年=100)',\n",
       " 'A09030L': '干鲜瓜果类农村居民消费价格指数(上年=100)',\n",
       " 'A09030M': '糕点饼干类农村居民消费价格指数(上年=100)',\n",
       " 'A09030N': '液体乳及乳制品类农村居民消费价格指数(上年=100)',\n",
       " 'A09030O': '在外用膳食品类农村居民消费价格指数(上年=100)',\n",
       " 'A09030P': '其他食品类农村居民消费价格指数(上年=100)',\n",
       " 'A09030Q': '烟酒及饮料类农村居民消费价格指数(上年=100)',\n",
       " 'A09030R': '烟草类农村居民消费价格指数(上年=100)',\n",
       " 'A09030S': '酒类农村居民消费价格指数(上年=100)',\n",
       " 'A09030U': '衣着类农村居民消费价格指数(上年=100)',\n",
       " 'A09030V': '服装类农村居民消费价格指数(上年=100)',\n",
       " 'A09030W': '男式服装类农村居民消费价格指数(上年=100)',\n",
       " 'A09030X': '女式服装类农村居民消费价格指数(上年=100)',\n",
       " 'A09030Y': '儿童服装类农村居民消费价格指数(上年=100)',\n",
       " 'A09030Z': '衣着材料类农村居民消费价格指数(上年=100)',\n",
       " 'A090310': '鞋袜帽类农村居民消费价格指数(上年=100)',\n",
       " 'A090311': '鞋类农村居民消费价格指数(上年=100)',\n",
       " 'A090312': '袜子类农村居民消费价格指数(上年=100)',\n",
       " 'A090313': '帽子类农村居民消费价格指数(上年=100)',\n",
       " 'A090314': '衣着加工服务费类农村居民消费价格指数(上年=100)',\n",
       " 'A090315': '家庭设备用品及维修服务类农村居民消费价格指数(上年=100)',\n",
       " 'A090316': '耐用消费品类农村居民消费价格指数(上年=100)',\n",
       " 'A090317': '家具类农村居民消费价格指数(上年=100)',\n",
       " 'A090318': '家庭设备类农村居民消费价格指数(上年=100)',\n",
       " 'A090319': '室内装饰品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031A': '床上用品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031B': '家庭日用杂品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031C': '家庭服务及加工维修服务类农村居民消费价格指数(上年=100)',\n",
       " 'A09031D': '医疗保健和个人用品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031E': '医疗保健类农村居民消费价格指数(上年=100)',\n",
       " 'A09031F': '医疗器具及用品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031G': '中药材及中成药类农村居民消费价格指数(上年=100)',\n",
       " 'A09031H': '西药类农村居民消费价格指数(上年=100)',\n",
       " 'A09031I': '保健器具及用品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031J': '医疗保健服务费类农村居民消费价格指数(上年=100)',\n",
       " 'A09031K': '个人用品及服务类农村居民消费价格指数(上年=100)',\n",
       " 'A09031L': '化妆美容用品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031M': '清洁化妆用品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031N': '个人饰品类农村居民消费价格指数(上年=100)',\n",
       " 'A09031O': '个人服务类农村居民消费价格指数(上年=100)',\n",
       " 'A09031P': '交通和通信类农村居民消费价格指数(上年=100)',\n",
       " 'A09031Q': '交通类农村居民消费价格指数(上年=100)',\n",
       " 'A09031R': '交通工具类农村居民消费价格指数(上年=100)',\n",
       " 'A09031S': '车用燃料及零配件类农村居民消费价格指数(上年=100)',\n",
       " 'A09031T': '车辆使用及维修费类农村居民消费价格指数(上年=100)',\n",
       " 'A09031U': '市内公共交通费类农村居民消费价格指数(上年=100)',\n",
       " 'A09031V': '城市间交通费类农村居民消费价格指数(上年=100)',\n",
       " 'A09031W': '通信类农村居民消费价格指数(上年=100)',\n",
       " 'A09031X': '通信工具类农村居民消费价格指数(上年=100)',\n",
       " 'A09031Y': '通信服务类农村居民消费价格指数(上年=100)',\n",
       " 'A09031Z': '娱乐教育文化用品及服务类农村居民消费价格指数(上年=100)',\n",
       " 'A090320': '文娱用耐用消费品及服务类农村居民消费价格指数(上年=100)',\n",
       " 'A090321': '教育类农村居民消费价格指数(上年=100)',\n",
       " 'A090322': '教材及参考书类农村居民消费价格指数(上年=100)',\n",
       " 'A090323': '学杂托幼费类农村居民消费价格指数(上年=100)',\n",
       " 'A090324': '文化娱乐类农村居民消费价格指数(上年=100)',\n",
       " 'A090325': '文化娱乐用品类农村居民消费价格指数(上年=100)',\n",
       " 'A090326': '书报杂志类农村居民消费价格指数(上年=100)',\n",
       " 'A090327': '文娱费类农村居民消费价格指数(上年=100)',\n",
       " 'A090328': '旅游类农村居民消费价格指数(上年=100)',\n",
       " 'A090329': '居住类农村居民消费价格指数(上年=100)',\n",
       " 'A09032A': '建房及装修材料类农村居民消费价格指数(上年=100)',\n",
       " 'A09032B': '租房类农村居民消费价格指数(上年=100)',\n",
       " 'A09032C': '自有住房类农村居民消费价格指数(上年=100)',\n",
       " 'A09032D': '水电燃料类农村居民消费价格指数(上年=100)',\n",
       " 'A090401': '商品零售价格指数(上年=100)',\n",
       " 'A090402': '食品类商品零售价格指数(上年=100)',\n",
       " 'A090403': '粮食类商品零售价格指数(上年=100)',\n",
       " 'A090404': '油脂类商品零售价格指数(上年=100)',\n",
       " 'A090405': '肉禽及其制品类商品零售价格指数(上年=100)',\n",
       " 'A090406': '蛋类商品零售价格指数(上年=100)',\n",
       " 'A090407': '水产品类商品零售价格指数(上年=100)',\n",
       " 'A090408': '菜类商品零售价格指数(上年=100)',\n",
       " 'A090409': '干鲜瓜果类商品零售价格指数(上年=100)',\n",
       " 'A09040A': '饮料烟酒类商品零售价格指数(上年=100)',\n",
       " 'A09040B': '服装鞋帽类商品零售价格指数(上年=100)',\n",
       " 'A09040C': '纺织品类商品零售价格指数(上年=100)',\n",
       " 'A09040D': '家用电器及音像器材类商品零售价格指数(上年=100)',\n",
       " 'A09040E': '文化办公用品类商品零售价格指数(上年=100)',\n",
       " 'A09040F': '日用品类商品零售价格指数(上年=100)',\n",
       " 'A09040G': '体育娱乐用品类商品零售价格指数(上年=100)',\n",
       " 'A09040H': '交通、通信用品类商品零售价格指数(上年=100)',\n",
       " 'A09040I': '家具类商品零售价格指数(上年=100)',\n",
       " 'A09040J': '化妆品类商品零售价格指数(上年=100)',\n",
       " 'A09040K': '金银珠宝类商品零售价格指数(上年=100)',\n",
       " 'A09040L': '中西药品及医疗保健用品类商品零售价格指数(上年=100)',\n",
       " 'A09040M': '书报杂志及电子出版物类商品零售价格指数(上年=100)',\n",
       " 'A09040N': '燃料类商品零售价格指数(上年=100)',\n",
       " 'A09040O': '建筑材料及五金电料类商品零售价格指数(上年=100)',\n",
       " 'A090501': '农村商品零售价格指数(上年=100)',\n",
       " 'A090502': '食品类农村商品零售价格指数(上年=100)',\n",
       " 'A090503': '粮食类农村商品零售价格指数(上年=100)',\n",
       " 'A090504': '淀粉类农村商品零售价格指数(上年=100)',\n",
       " 'A090505': '干豆类及豆制品类农村商品零售价格指数(上年=100)',\n",
       " 'A090506': '油脂类农村商品零售价格指数(上年=100)',\n",
       " 'A090507': '肉禽及其制品类农村商品零售价格指数(上年=100)',\n",
       " 'A090508': '蛋类农村商品零售价格指数(上年=100)',\n",
       " 'A090509': '水产品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050A': '菜类农村商品零售价格指数(上年=100)',\n",
       " 'A09050B': '调味品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050C': '糖类农村商品零售价格指数(上年=100)',\n",
       " 'A09050D': '干鲜瓜果类农村商品零售价格指数(上年=100)',\n",
       " 'A09050E': '糕点饼干面包类农村商品零售价格指数(上年=100)',\n",
       " 'A09050F': '液体乳及乳制品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050G': '在外用膳食品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050H': '其他食品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050I': '饮料、烟酒类农村商品零售价格指数(上年=100)',\n",
       " 'A09050J': '茶及饮料类农村商品零售价格指数(上年=100)',\n",
       " 'A09050K': '烟草类农村商品零售价格指数(上年=100)',\n",
       " 'A09050L': '酒类农村商品零售价格指数(上年=100)',\n",
       " 'A09050M': '服装、鞋帽类农村商品零售价格指数(上年=100)',\n",
       " 'A09050N': '服装类农村商品零售价格指数(上年=100)',\n",
       " 'A09050O': '鞋袜帽类农村商品零售价格指数(上年=100)',\n",
       " 'A09050P': '其他类农村商品零售价格指数(上年=100)',\n",
       " 'A09050Q': '纺织品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050R': '衣着材料类农村商品零售价格指数(上年=100)',\n",
       " 'A09050S': '床上用品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050T': '家用电器及音像器材类农村商品零售价格指数(上年=100)',\n",
       " 'A09050U': '家庭设备类农村商品零售价格指数(上年=100)',\n",
       " 'A09050V': '文娱用耐用消费品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050W': '音像器材类农村商品零售价格指数(上年=100)',\n",
       " 'A09050X': '文化办公用品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050Y': '日用品类农村商品零售价格指数(上年=100)',\n",
       " 'A09050Z': '日用百货类农村商品零售价格指数(上年=100)',\n",
       " 'A090510': '日用杂品类农村商品零售价格指数(上年=100)',\n",
       " 'A090511': '洗涤用品类农村商品零售价格指数(上年=100)',\n",
       " 'A090512': '其他日用品类农村商品零售价格指数(上年=100)',\n",
       " 'A090513': '体育娱乐用品类农村商品零售价格指数(上年=100)',\n",
       " 'A090514': '体育用品类农村商品零售价格指数(上年=100)',\n",
       " 'A090515': '娱乐用品类农村商品零售价格指数(上年=100)',\n",
       " 'A090516': '交通、通信用品类农村商品零售价格指数(上年=100)',\n",
       " 'A090517': '交通运输机械类农村商品零售价格指数(上年=100)',\n",
       " 'A090518': '通讯器材类农村商品零售价格指数(上年=100)',\n",
       " 'A090519': '家具类农村商品零售价格指数(上年=100)',\n",
       " 'A09051A': '化妆品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051B': '金银珠宝类农村商品零售价格指数(上年=100)',\n",
       " 'A09051C': '中西药品及医疗保健用品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051D': '医疗器具及用品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051E': '中药材及中成药类农村商品零售价格指数(上年=100)',\n",
       " 'A09051F': '西药类农村商品零售价格指数(上年=100)',\n",
       " 'A09051G': '保健器具及用品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051H': '书报杂志及电子出版物类农村商品零售价格指数(上年=100)',\n",
       " 'A09051I': '教材及参考书类农村商品零售价格指数(上年=100)',\n",
       " 'A09051J': '书报杂志类农村商品零售价格指数(上年=100)',\n",
       " 'A09051K': '电子音像制品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051L': '燃料类农村商品零售价格指数(上年=100)',\n",
       " 'A09051M': '煤炭及制品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051N': '石油及制品类农村商品零售价格指数(上年=100)',\n",
       " 'A09051O': '建筑材料及五金电料类农村商品零售价格指数(上年=100)',\n",
       " 'A09051P': '建筑装璜材料类农村商品零售价格指数(上年=100)',\n",
       " 'A09051Q': '五金电料类农村商品零售价格指数(上年=100)',\n",
       " 'A090601': '农业生产资料指数(上年=100)',\n",
       " 'A090602': '农用手工工具指数(上年=100)',\n",
       " 'A090603': '饲料价格指数(上年=100)',\n",
       " 'A090604': '产品畜价格指数(上年=100)',\n",
       " 'A090605': '半机械化农具生产资料价格指数(上年=100)',\n",
       " 'A090606': '机械化农具生产资料价格指数(上年=100)',\n",
       " 'A090607': '化学肥料生产资料价格指数(上年=100)',\n",
       " 'A090608': '农药及农药械生产资料价格指数(上年=100)',\n",
       " 'A090609': '化学农药生产资料价格指数(上年=100)',\n",
       " 'A09060A': '农药器械生产资料价格指数(上年=100)',\n",
       " 'A09060B': '农用机油生产资料价格指数(上年=100)',\n",
       " 'A09060C': '其他农业生产资料价格指数(上年=100)',\n",
       " 'A09060D': '农用种子生产资料价格指数(上年=100)',\n",
       " 'A09060E': '其他农业生产资料(除种子)价格指数(上年=100)',\n",
       " 'A09060F': '农业生产服务价格指数(上年=100)',\n",
       " 'A090701': '农产品生产价格指数(上年=100)',\n",
       " 'A090702': '种植业生产价格指数(上年价格=100)',\n",
       " 'A090703': '谷物生产价格指数(上年价格=100)',\n",
       " 'A090704': '小麦生产价格指数(上年价格=100)',\n",
       " 'A090705': '稻谷生产价格指数(上年价格=100)',\n",
       " 'A090706': '玉米生产价格指数(上年价格=100)',\n",
       " 'A090707': '豆类生产价格指数(上年价格=100)',\n",
       " 'A090708': '大豆生产价格指数(上年价格=100)',\n",
       " 'A090709': '薯类生产价格指数(上年价格=100)',\n",
       " 'A09070A': '油料生产价格指数(上年价格=100)',\n",
       " 'A09070B': '花生生产价格指数(上年价格=100)',\n",
       " 'A09070C': '油菜籽生产价格指数(上年价格=100)',\n",
       " 'A09070D': '棉花生产价格指数(上年价格=100)',\n",
       " 'A09070E': '糖料生产价格指数(上年价格=100)',\n",
       " 'A09070F': '麻类生产价格指数(上年价格=100)',\n",
       " 'A09070G': '烟叶生产价格指数(上年价格=100)',\n",
       " 'A09070H': '蔬菜生产价格指数(上年价格=100)',\n",
       " 'A09070I': '水果生产价格指数(上年价格=100)',\n",
       " 'A09070J': '茶叶生产价格指数(上年价格=100)',\n",
       " 'A09070K': '林产品生产价格指数(上年价格=100)',\n",
       " 'A09070L': '畜产品生产价格指数(上年价格=100)',\n",
       " 'A09070M': '猪生产价格指数(上年价格=100)',\n",
       " 'A09070N': '家禽生产价格指数(上年价格=100)',\n",
       " 'A09070O': '蛋类生产价格指数(上年价格=100)',\n",
       " 'A09070P': '奶类生产价格指数(上年价格=100)',\n",
       " 'A09070Q': '渔业产品生产价格指数(上年价格=100)',\n",
       " 'A09070R': '海水养殖产品生产价格指数(上年=100)',\n",
       " 'A09070S': '海水捕捞产品生产价格指数(上年=100)',\n",
       " 'A09070T': '淡水养殖产品生产价格指数(上年=100)',\n",
       " 'A09070U': '淡水捕捞产品生产价格指数(上年=100)',\n",
       " 'A090801': '工业生产者出厂价格指数(上年=100)',\n",
       " 'A090901': '固定资产投资价格指数(上年=100)',\n",
       " 'A090902': '建筑安装工程固定资产投资价格指数(上年=100)',\n",
       " 'A090903': '设备工器具购置固定资产投资价格指数(上年=100)',\n",
       " 'A090904': '其他费用固定资产投资价格指数(上年=100)',\n",
       " 'A090A01': '食品烟酒类居民消费价格指数(上年=100)',\n",
       " 'A090A02': '食品类居民消费价格指数(上年=100)',\n",
       " 'A090A03': '粮食类居民消费价格指数(上年=100)',\n",
       " 'A090A04': '薯类类居民消费价格指数(上年=100)',\n",
       " 'A090A05': '豆类类居民消费价格指数(上年=100)',\n",
       " 'A090A06': '食用油类居民消费价格指数(上年=100)',\n",
       " 'A090A07': '菜类居民消费价格指数(上年=100)',\n",
       " 'A090A08': '鲜菜类居民消费价格指数(上年=100)',\n",
       " 'A090A09': '畜肉类类居民消费价格指数(上年=100)',\n",
       " 'A090A0A': '禽肉类类居民消费价格指数(上年=100)',\n",
       " 'A090A0B': '水产品类居民消费价格指数(上年=100)',\n",
       " 'A090A0C': '蛋类类居民消费价格指数(上年=100)',\n",
       " 'A090A0D': '奶类类居民消费价格指数(上年=100)',\n",
       " 'A090A0E': '干鲜瓜果类类居民消费价格指数(上年=100)',\n",
       " 'A090A0F': '鲜瓜果类居民消费价格指数(上年=100)',\n",
       " 'A090A0G': '糖果糕点类类居民消费价格指数(上年=100)',\n",
       " 'A090A0H': '调味品类居民消费价格指数(上年=100)',\n",
       " 'A090A0I': '其他食品类类居民消费价格指数(上年=100)',\n",
       " 'A090A0J': '茶及饮料类居民消费价格指数(上年=100)',\n",
       " 'A090A0K': '烟酒类居民消费价格指数(上年=100)',\n",
       " 'A090A0L': '在外餐饮类居民消费价格指数(上年=100)',\n",
       " 'A090A0M': '衣着类居民消费价格指数(上年=100)',\n",
       " 'A090A0N': '服装类居民消费价格指数(上年=100)',\n",
       " 'A090A0O': '服装材料类居民消费价格指数(上年=100)',\n",
       " 'A090A0P': '其他衣着及配件类居民消费价格指数(上年=100)',\n",
       " 'A090A0Q': '衣着加工服务费类居民消费价格指数(上年=100)',\n",
       " 'A090A0R': '鞋类类居民消费价格指数(上年=100)',\n",
       " 'A090A0S': '居住类居民消费价格指数(上年=100)',\n",
       " 'A090A0T': '租赁房房租类居民消费价格指数(上年=100)',\n",
       " 'A090A0U': '住房保养维修及管理类居民消费价格指数(上年=100)',\n",
       " 'A090A0V': '水电燃料类居民消费价格指数(上年=100)',\n",
       " 'A090A0W': '自有住房类居民消费价格指数(上年=100)',\n",
       " 'A090A0X': '生活用品及服务类居民消费价格指数(上年=100)',\n",
       " 'A090A0Y': '家具及室内装饰品类居民消费价格指数(上年=100)',\n",
       " 'A090A0Z': '家用器具类居民消费价格指数(上年=100)',\n",
       " 'A090A10': '家用纺织品类居民消费价格指数(上年=100)',\n",
       " 'A090A11': '家庭日用杂品类居民消费价格指数(上年=100)',\n",
       " 'A090A12': '个人护理用品类居民消费价格指数(上年=100)',\n",
       " 'A090A13': '家庭服务类居民消费价格指数(上年=100)',\n",
       " 'A090A14': '交通和通信类居民消费价格指数(上年=100)',\n",
       " 'A090A15': '交通类居民消费价格指数(上年=100)',\n",
       " 'A090A16': '交通工具类居民消费价格指数(上年=100)',\n",
       " 'A090A17': '交通工具用燃料类居民消费价格指数(上年=100)',\n",
       " 'A090A18': '交通工具使用和维修类居民消费价格指数(上年=100)',\n",
       " 'A090A19': '交通费类居民消费价格指数(上年=100)',\n",
       " 'A090A1A': '通信类居民消费价格指数(上年=100)',\n",
       " 'A090A1B': '教育文化和娱乐类居民消费价格指数(上年=100)',\n",
       " 'A090A1C': '教育类居民消费价格指数(上年=100)',\n",
       " 'A090A1D': '教育用品类居民消费价格指数(上年=100)',\n",
       " 'A090A1E': '教育服务类居民消费价格指数(上年=100)',\n",
       " 'A090A1F': '文化娱乐类居民消费价格指数(上年=100)',\n",
       " 'A090A1G': '文娱耐用消费品类居民消费价格指数(上年=100)',\n",
       " 'A090A1H': '其他文娱用品类居民消费价格指数(上年=100)',\n",
       " 'A090A1I': '文化娱乐服务类居民消费价格指数(上年=100)',\n",
       " 'A090A1J': '旅游类居民消费价格指数(上年=100)',\n",
       " 'A090A1K': '医疗保健类居民消费价格指数(上年=100)',\n",
       " 'A090A1L': '药品及医疗器具类居民消费价格指数(上年=100)',\n",
       " 'A090A1M': '医疗服务类居民消费价格指数(上年=100)',\n",
       " 'A090A1N': '其他用品和服务类居民消费价格指数(上年=100)',\n",
       " 'A090A1O': '其他用品类类居民消费价格指数(上年=100)',\n",
       " 'A090A1P': '其他服务类类居民消费价格指数(上年=100)',\n",
       " 'A0A0001': '居民人均可支配收入',\n",
       " 'A0A0002': '居民人均可支配收入_同比增长',\n",
       " 'A0A0003': '城镇居民人均可支配收入',\n",
       " 'A0A0004': '城镇居民人均可支配收入_同比增长',\n",
       " 'A0A0005': '农村居民人均可支配收入',\n",
       " 'A0A0006': '农村居民人均可支配收入_同比增长',\n",
       " 'A0A0007': '居民人均消费支出',\n",
       " 'A0A0008': '居民人均消费支出_同比增长',\n",
       " 'A0A0009': '城镇居民人均消费支出',\n",
       " 'A0A0010': '城镇居民人均消费支出_同比增长',\n",
       " 'A0A0011': '农村居民人均消费支出',\n",
       " 'A0A0012': '农村居民人均消费支出_同比增长',\n",
       " 'A0A0101': '城乡居民人民币储蓄存款年底余额',\n",
       " 'A0A0201': '城镇居民人均可支配收入',\n",
       " 'A0A0202': '城镇居民人均总收入',\n",
       " 'A0A0203': '城镇居民人均工资性收入',\n",
       " 'A0A0204': '城镇居民人均经营净收入',\n",
       " 'A0A0205': '城镇居民人均财产性收入',\n",
       " 'A0A0206': '城镇居民人均转移性收入',\n",
       " 'A0A0301': '城镇居民家庭人均现金消费支出',\n",
       " 'A0A0302': '城镇居民家庭人均食品消费支出',\n",
       " 'A0A030L': '城镇居民家庭人均衣着消费支出',\n",
       " 'A0A030Q': '城镇居民家庭人均居住消费支出',\n",
       " 'A0A030T': '城镇居民家庭人均家庭设备及用品消费支出',\n",
       " 'A0A0310': '城镇居民家庭人均医疗保健消费支出',\n",
       " 'A0A0311': '城镇居民家庭人均交通和通信消费支出',\n",
       " 'A0A0314': '城镇居民家庭人均文教娱乐服务消费支出',\n",
       " 'A0A0318': '城镇居民家庭人均其它消费支出',\n",
       " 'A0A0401': '城镇居民家庭平均每百户摩托车拥有量',\n",
       " 'A0A0402': '城镇居民家庭平均每百户助力车拥有量',\n",
       " 'A0A0403': '城镇居民家庭平均每百户家用汽车拥有量',\n",
       " 'A0A0404': '城镇居民家庭平均每百户洗衣机拥有量',\n",
       " 'A0A0405': '城镇居民家庭平均每百户电冰箱拥有量',\n",
       " 'A0A0406': '城镇居民家庭平均每百户彩色电视机拥有量',\n",
       " 'A0A0407': '城镇居民家庭平均每百户计算机拥有量',\n",
       " 'A0A0408': '城镇居民家庭平均每百户组合音响拥有量',\n",
       " 'A0A0409': '城镇居民家庭平均每百户摄像机拥有量',\n",
       " 'A0A040A': '城镇居民家庭平均每百户照相机拥有量',\n",
       " 'A0A040B': '城镇居民家庭平均每百户钢琴拥有量',\n",
       " 'A0A040C': '城镇居民家庭平均每百户其他中高档乐器拥有量',\n",
       " 'A0A040D': '城镇居民家庭平均每百户微波炉拥有量',\n",
       " 'A0A040E': '城镇居民家庭平均每百户空调拥有量',\n",
       " 'A0A040F': '城镇居民家庭平均每百户淋浴热水器拥有量',\n",
       " 'A0A040G': '城镇居民家庭平均每百户消毒碗柜拥有量',\n",
       " 'A0A040H': '城镇居民家庭平均每百户洗碗机拥有量',\n",
       " 'A0A040I': '城镇居民家庭平均每百户健身器材拥有量',\n",
       " 'A0A040J': '城镇居民家庭平均每百户固定电话拥有量',\n",
       " 'A0A040K': '城镇居民家庭平均每百户移动电话拥有量',\n",
       " 'A0A0501': '农村居民家庭人均纯收入',\n",
       " 'A0A0502': '农村居民家庭人均工资性纯收入',\n",
       " 'A0A0503': '农村居民家庭人均家庭经营纯收入',\n",
       " 'A0A0504': '农村居民家庭人均财产性纯收入',\n",
       " 'A0A0505': '农村居民家庭人均转移性纯收入',\n",
       " 'A0A0601': '农村居民家庭平均每人消费支出',\n",
       " 'A0A0602': '农村居民家庭平均每人食品消费支出',\n",
       " 'A0A0603': '农村居民家庭平均每人衣着消费支出',\n",
       " 'A0A0604': '农村居民家庭平均每人居住消费支出',\n",
       " 'A0A0605': '农村居民家庭平均每人家庭设备及用品消费支出',\n",
       " 'A0A0606': '农村居民家庭平均每人交通通信消费支出',\n",
       " 'A0A0607': '农村居民家庭平均每人文教娱乐消费支出',\n",
       " 'A0A0608': '农村居民家庭平均每人医疗保健消费支出',\n",
       " 'A0A0609': '农村居民家庭平均每人其他消费支出',\n",
       " 'A0A0901': '农村居民家庭平均每百户洗衣机拥有量',\n",
       " 'A0A0902': '农村居民家庭平均每百户电冰箱拥有量',\n",
       " 'A0A0903': '农村居民家庭平均每百户空调拥有量',\n",
       " 'A0A0904': '农村居民家庭平均每百户抽油烟机拥有量',\n",
       " 'A0A0905': '农村居民家庭平均每百户自行车拥有量',\n",
       " 'A0A0906': '农村居民家庭平均每百户摩托车拥有量',\n",
       " 'A0A0907': '农村居民家庭平均每百户固定电话拥有量',\n",
       " 'A0A0908': '农村居民家庭平均每百户移动电话拥有量',\n",
       " 'A0A0909': '农村居民家庭平均每百户黑白电视机拥有量',\n",
       " 'A0A090A': '农村居民家庭平均每百户彩色电视机拥有量',\n",
       " 'A0A090B': '农村居民家庭平均每百户照相机拥有量',\n",
       " 'A0A090C': '农村居民家庭平均每百户计算机拥有量',\n",
       " 'A0A0A01': '农村居民人均住房面积',\n",
       " 'A0A0A02': '农村居民家庭住房价值',\n",
       " 'A0A0A03': '农村居民家庭住房钢筋混凝土结构',\n",
       " 'A0A0A04': '农村居民家庭住房砖木结构',\n",
       " 'A0B0101': '全部地级及以上城市数',\n",
       " 'A0B0102': '城市市辖区年末总人口为400万以上的地级及以上城市数',\n",
       " 'A0B0103': '城市市辖区年末总人口为200-400万的地级及以上城市数',\n",
       " 'A0B0104': '城市市辖区年末总人口为100-200万的地级及以上城市数',\n",
       " 'A0B0105': '城市市辖区年末总人口为50-100万的地级及以上城市数',\n",
       " 'A0B0106': '城市市辖区年末总人口为20-50万的地级及以上城市数',\n",
       " 'A0B0107': '城市市辖区年末总人口为20万以下的地级及以上城市数',\n",
       " 'A0B0201': '城区面积',\n",
       " ...}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "指标字典 =df_m['cname'].to_dict()\n",
    "指标字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{110000: '北京市',\n",
       " 120000: '天津市',\n",
       " 130000: '河北省',\n",
       " 140000: '山西省',\n",
       " 150000: '内蒙古自治区',\n",
       " 210000: '辽宁省',\n",
       " 220000: '吉林省',\n",
       " 230000: '黑龙江省',\n",
       " 310000: '上海市',\n",
       " 320000: '江苏省',\n",
       " 330000: '浙江省',\n",
       " 340000: '安徽省',\n",
       " 350000: '福建省',\n",
       " 360000: '江西省',\n",
       " 370000: '山东省',\n",
       " 410000: '河南省',\n",
       " 420000: '湖北省',\n",
       " 430000: '湖南省',\n",
       " 440000: '广东省',\n",
       " 450000: '广西壮族自治区',\n",
       " 460000: '海南省',\n",
       " 500000: '重庆市',\n",
       " 510000: '四川省',\n",
       " 520000: '贵州省',\n",
       " 530000: '云南省',\n",
       " 540000: '西藏自治区',\n",
       " 610000: '陕西省',\n",
       " 620000: '甘肃省',\n",
       " 630000: '青海省',\n",
       " 640000: '宁夏回族自治区',\n",
       " 650000: '新疆维吾尔自治区'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "地区字典=df_r['name'].to_dict()\n",
    "地区字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reg</th>\n",
       "      <th>sj</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zb</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>地级区划数</th>\n",
       "      <td>110000</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地级区划数</th>\n",
       "      <td>110000</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地级区划数</th>\n",
       "      <td>110000</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地级区划数</th>\n",
       "      <td>110000</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地级区划数</th>\n",
       "      <td>110000</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城乡居民社会养老保险累计结余</th>\n",
       "      <td>650000</td>\n",
       "      <td>2013</td>\n",
       "      <td>33.856600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城乡居民社会养老保险累计结余</th>\n",
       "      <td>650000</td>\n",
       "      <td>2012</td>\n",
       "      <td>24.914044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城乡居民社会养老保险累计结余</th>\n",
       "      <td>650000</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城乡居民社会养老保险累计结余</th>\n",
       "      <td>650000</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城乡居民社会养老保险累计结余</th>\n",
       "      <td>650000</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>908300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   reg    sj       data\n",
       "zb                                     \n",
       "地级区划数           110000  2018        NaN\n",
       "地级区划数           110000  2017        NaN\n",
       "地级区划数           110000  2016        NaN\n",
       "地级区划数           110000  2015        NaN\n",
       "地级区划数           110000  2014        NaN\n",
       "...                ...   ...        ...\n",
       "城乡居民社会养老保险累计结余  650000  2013  33.856600\n",
       "城乡居民社会养老保险累计结余  650000  2012  24.914044\n",
       "城乡居民社会养老保险累计结余  650000  2011        NaN\n",
       "城乡居民社会养老保险累计结余  650000  2010        NaN\n",
       "城乡居民社会养老保险累计结余  650000  2009        NaN\n",
       "\n",
       "[908300 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =df_raw.reset_index().set_index(\"zb\").rename(index=指标字典)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zb</th>\n",
       "      <th>sj</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2013</td>\n",
       "      <td>33.856600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2012</td>\n",
       "      <td>24.914044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>908300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      zb    sj       data\n",
       "reg                                      \n",
       "北京市                地级区划数  2018        NaN\n",
       "北京市                地级区划数  2017        NaN\n",
       "北京市                地级区划数  2016        NaN\n",
       "北京市                地级区划数  2015        NaN\n",
       "北京市                地级区划数  2014        NaN\n",
       "...                  ...   ...        ...\n",
       "新疆维吾尔自治区  城乡居民社会养老保险累计结余  2013  33.856600\n",
       "新疆维吾尔自治区  城乡居民社会养老保险累计结余  2012  24.914044\n",
       "新疆维吾尔自治区  城乡居民社会养老保险累计结余  2011        NaN\n",
       "新疆维吾尔自治区  城乡居民社会养老保险累计结余  2010        NaN\n",
       "新疆维吾尔自治区  城乡居民社会养老保险累计结余  2009        NaN\n",
       "\n",
       "[908300 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index().set_index('reg').rename(index=地区字典)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>指标</th>\n",
       "      <th>年</th>\n",
       "      <th>数据</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京市</td>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>北京市</td>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>北京市</td>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>北京市</td>\n",
       "      <td>地级区划数</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908295</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2013</td>\n",
       "      <td>33.856600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908296</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2012</td>\n",
       "      <td>24.914044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908297</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908298</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908299</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>城乡居民社会养老保险累计结余</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>908300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              地区              指标     年         数据\n",
       "0            北京市           地级区划数  2018        NaN\n",
       "1            北京市           地级区划数  2017        NaN\n",
       "2            北京市           地级区划数  2016        NaN\n",
       "3            北京市           地级区划数  2015        NaN\n",
       "4            北京市           地级区划数  2014        NaN\n",
       "...          ...             ...   ...        ...\n",
       "908295  新疆维吾尔自治区  城乡居民社会养老保险累计结余  2013  33.856600\n",
       "908296  新疆维吾尔自治区  城乡居民社会养老保险累计结余  2012  24.914044\n",
       "908297  新疆维吾尔自治区  城乡居民社会养老保险累计结余  2011        NaN\n",
       "908298  新疆维吾尔自治区  城乡居民社会养老保险累计结余  2010        NaN\n",
       "908299  新疆维吾尔自治区  城乡居民社会养老保险累计结余  2009        NaN\n",
       "\n",
       "[908300 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zh = df.reset_index().rename(columns={\"zb\":\"指标\",\"reg\":\"地区\",\\\n",
    "                                       \"sj\":\"年\",\"data\":\"数据\",})\n",
    "df_zh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# df_zh.set_index(\"指标\").loc[\"国有城镇单位就业人员工资总额\",\"年\":\"数据\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>指标</th>\n",
       "      <th>年</th>\n",
       "      <th>数据</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151280</th>\n",
       "      <td>北京市</td>\n",
       "      <td>煤炭消费量</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151281</th>\n",
       "      <td>北京市</td>\n",
       "      <td>煤炭消费量</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151282</th>\n",
       "      <td>北京市</td>\n",
       "      <td>煤炭消费量</td>\n",
       "      <td>2016</td>\n",
       "      <td>847.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151283</th>\n",
       "      <td>北京市</td>\n",
       "      <td>煤炭消费量</td>\n",
       "      <td>2015</td>\n",
       "      <td>1165.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151284</th>\n",
       "      <td>北京市</td>\n",
       "      <td>煤炭消费量</td>\n",
       "      <td>2014</td>\n",
       "      <td>1736.543192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154065</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>电力消费量</td>\n",
       "      <td>2013</td>\n",
       "      <td>1539.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154066</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>电力消费量</td>\n",
       "      <td>2012</td>\n",
       "      <td>1151.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154067</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>电力消费量</td>\n",
       "      <td>2011</td>\n",
       "      <td>839.104400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154068</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>电力消费量</td>\n",
       "      <td>2010</td>\n",
       "      <td>661.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154069</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>电力消费量</td>\n",
       "      <td>2009</td>\n",
       "      <td>547.876600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2790 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              地区     指标     年           数据\n",
       "151280       北京市  煤炭消费量  2018          NaN\n",
       "151281       北京市  煤炭消费量  2017          NaN\n",
       "151282       北京市  煤炭消费量  2016   847.620000\n",
       "151283       北京市  煤炭消费量  2015  1165.180000\n",
       "151284       北京市  煤炭消费量  2014  1736.543192\n",
       "...          ...    ...   ...          ...\n",
       "154065  新疆维吾尔自治区  电力消费量  2013  1539.800000\n",
       "154066  新疆维吾尔自治区  电力消费量  2012  1151.500000\n",
       "154067  新疆维吾尔自治区  电力消费量  2011   839.104400\n",
       "154068  新疆维吾尔自治区  电力消费量  2010   661.960000\n",
       "154069  新疆维吾尔自治区  电力消费量  2009   547.876600\n",
       "\n",
       "[2790 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按问题意识去切数据\n",
    "dslice =df_zh[ df_zh.指标.str.contains(\"消费量\")]\n",
    "dslice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['煤炭消费量', '焦炭消费量', '原油消费量', '汽油消费量', '煤油消费量', '柴油消费量', '燃料油消费量',\n",
       "       '天然气消费量', '电力消费量'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "指标分的可能性 = dslice.指标.unique()\n",
    "指标分的可能性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['煤炭', ''],\n",
       " ['焦炭', ''],\n",
       " ['原油', ''],\n",
       " ['汽油', ''],\n",
       " ['煤油', ''],\n",
       " ['柴油', ''],\n",
       " ['燃料油', ''],\n",
       " ['天然气', ''],\n",
       " ['电力', '']]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "指标分的可能性 = [x.split(\"消费量\") for x in dslice.指标.unique() ]\n",
    "指标分的可能性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['汽油', '']]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "指标分的可能性_取 = [ [x,y] for (x,y) in 指标分的可能性 if  y ==''and x =='汽油' ]\n",
    "指标分的可能性_取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['汽油消费量']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "指标分的可能性_取_all = [\"消费量\".join(x) for x in 指标分的可能性_取]\n",
    "指标分的可能性_取_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>指标</th>\n",
       "      <th>地区</th>\n",
       "      <th>年</th>\n",
       "      <th>数据</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>北京市</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>北京市</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>北京市</td>\n",
       "      <td>2016</td>\n",
       "      <td>470.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>北京市</td>\n",
       "      <td>2015</td>\n",
       "      <td>462.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>北京市</td>\n",
       "      <td>2014</td>\n",
       "      <td>440.6166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>2013</td>\n",
       "      <td>209.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>2012</td>\n",
       "      <td>154.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>2011</td>\n",
       "      <td>138.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>2010</td>\n",
       "      <td>131.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>汽油消费量</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>2009</td>\n",
       "      <td>121.8700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        指标        地区     年        数据\n",
       "0    汽油消费量       北京市  2018       NaN\n",
       "1    汽油消费量       北京市  2017       NaN\n",
       "2    汽油消费量       北京市  2016  470.3700\n",
       "3    汽油消费量       北京市  2015  462.7500\n",
       "4    汽油消费量       北京市  2014  440.6166\n",
       "..     ...       ...   ...       ...\n",
       "305  汽油消费量  新疆维吾尔自治区  2013  209.3600\n",
       "306  汽油消费量  新疆维吾尔自治区  2012  154.6300\n",
       "307  汽油消费量  新疆维吾尔自治区  2011  138.8400\n",
       "308  汽油消费量  新疆维吾尔自治区  2010  131.1700\n",
       "309  汽油消费量  新疆维吾尔自治区  2009  121.8700\n",
       "\n",
       "[310 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_汽油消费量切片 = df_zh.set_index(\"指标\").loc[指标分的可能性_取_all].reset_index()\n",
    "df_汽油消费量切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['KaiTi']\n",
    "mpl.rcParams['font.serif'] = ['KaiTi']\n",
    "mpl.rcParams['axes.titlesize'] = 24\n",
    "mpl.rcParams['xtick.labelsize'] = 18\n",
    "mpl.rcParams['ytick.labelsize'] = 18\n",
    "mpl.rcParams['legend.fontsize'] = 14\n",
    "mpl.rcParams['legend.title_fontsize'] = 18\n",
    "mpl.rcParams['legend.loc'] = 'upper right'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x258cdd07448>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_汽油消费量切片.set_index([\"地区\",\"指标\",\"年\"])\\\n",
    "       .unstack()\\\n",
    ".sort_values (by=('数据',2012),ascending=False)\\\n",
    ".droplevel(None, axis=1)\\\n",
    ".T\\\n",
    ".plot(figsize=(16,9),title='全国不同地区汽油消费量近10年分布变化图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_汽油消费量切片.query('年==2012')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-cdcf5eb0e81e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mprovinces_map\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpress\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m fig = px.choropleth_mapbox(\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "from urllib.request import urlopen\n",
    "import json\n",
    "with open(\"china_province.geojson\",encoding= \"utf-8\") as f:\n",
    "    provinces_map = json.load(f)\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "fig = px.choropleth_mapbox(\n",
    "    data_frame=df,\n",
    "    geojson=provinces_map,\n",
    "    color='数据',\n",
    "    locations=\"地区\",\n",
    "    featureidkey=\"properties.NL_NAME_1\",\n",
    "    mapbox_style=\"carto-darkmatter\",\n",
    "    color_continuous_scale='viridis',\n",
    "    center={\"lat\": 37.110573, \"lon\": 106.493924},\n",
    "    zoom=3,\n",
    ")\n",
    "fig.update_layout(\n",
    "    mapbox_style=\"carto-darkmatter\",\n",
    "    mapbox_zoom=3,\n",
    "    mapbox_center={\"lat\": 37.110573, \"lon\": 106.493924},\n",
    ")\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
